Explainable Artificial Intelligence in Data Analytics: Enhancing Transparency and Trust in Decision-Making Systems

Abstract

The widespread deployment of deep learning and complex ensemble models in high-stakes decision-making domains—including healthcare diagnostics, financial credit scoring, criminal justice risk assessment, and autonomous systems—has created an urgent demand for explainability, interpretability, and transparency in artificial intelligence (AI) systems. These opaque “black-box” models achieve state-of-the-art predictive performance but provide no human understandable rationale for their outputs, undermining user trust, impeding regulatory compliance under frameworks such as the EU AI Act and GDPR’s “right to explanation,” and precluding meaningful human oversight in safety critical applications. Explainable Artificial Intelligence (XAI) has emerged as a multidisciplinary research field developing methods, techniques, and frameworks that make AI decision-making processes transparent, interpretable, and trustworthy to human stakeholders. This paper presents a comprehensive review-based and experimental investigation of XAI methods for data analytics across healthcare, finance, and cybersecurity domains. A systematic review of 114 peer-reviewed publications (2019–2026) was supplemented by original experimental work at the AI Transparency and Trust Laboratory of Sandip Institute of Technology and Research Centre, Nashik, where a comparative evaluation of six post-hoc XAI methods—SHAP, LIME, Grad-CAM, Integrated Gradients, Attention Rollout, and counterfactual explanations—was conducted on three real-world classification tasks: breast cancer histopathology diagnosis (DenseNet-121), credit default prediction (XGBoost), and network intrusion detection (Random Forest). The evaluation employed a novel multi-dimensional XAI quality framework measuring explanation fidelity (faithfulness to model behavior), stability (consistency across perturbations), comprehensibility (human understandability via user study, n = 48 participants), and actionability (utility for decision improvement). SHAP achieved the highest fidelity scores across all three tasks (mean fidelity 0.924), while LIME demonstrated superior comprehensibility ratings from non-expert users (4.2/5.0). The findings establish that no single XAI method dominates across all quality dimensions, and that task-specific XAI method selection guided by stakeholder requirements and regulatory context is essential for responsible AI deployment [1], [2].

KEYWORDS: Explainable AI, Interpretable Machine Learning, SHAP, LIME, Grad-CAM, Transparency, Trust, Black-Box Models, Responsible AI, EU AI Act